{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TüEyeQ Analysis\n",
    "\n",
    "This notebook contains experiments and plots accompanying the TüEyeQ data set. Please refer to our paper when using this script:\n",
    "\n",
    "    [citation]\n",
    "\n",
    "The TüEyeQ data set can be downloaded at [link].\n",
    "\n",
    "This notebook comprises the following parts:\n",
    "1. Load Packages and Data\n",
    "2. General Analysis of the Raw Data (prior to pre-processing)\n",
    "3. Preprocessing\n",
    "4. Distance Correlations of Features\n",
    "5. Logistic Regression Model\n",
    "\n",
    "---\n",
    "This code is published under the MIT license.\n",
    "*Copyright (c) 2020 Johannes Haug*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Load Packages and Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import roc_auc_score, roc_curve\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import shap\n",
    "from lime import lime_tabular\n",
    "import dcor\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>task_id</th>\n",
       "      <th>subject</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>handedness</th>\n",
       "      <th>native_german</th>\n",
       "      <th>native_german_mother</th>\n",
       "      <th>native_language_mother</th>\n",
       "      <th>native_german_father</th>\n",
       "      <th>native_language_father</th>\n",
       "      <th>...</th>\n",
       "      <th>leisure_play_games</th>\n",
       "      <th>leisure_relaxation</th>\n",
       "      <th>leisure_social_activity</th>\n",
       "      <th>leisure_humanitarian_services</th>\n",
       "      <th>leisure_nature_activities</th>\n",
       "      <th>leisure_travel_tourism</th>\n",
       "      <th>study_subject_primary</th>\n",
       "      <th>study_subject_secondary</th>\n",
       "      <th>cft_task</th>\n",
       "      <th>cft_sum_full</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cft_rows_1</td>\n",
       "      <td>AAB14</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Türkisch</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Italienisch</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Social sciences, journalism and information</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>cft_rows_2</td>\n",
       "      <td>AAB14</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Türkisch</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Italienisch</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Social sciences, journalism and information</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cft_rows_3</td>\n",
       "      <td>AAB14</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Türkisch</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Italienisch</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Social sciences, journalism and information</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cft_rows_4_e</td>\n",
       "      <td>AAB14</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Türkisch</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Italienisch</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Social sciences, journalism and information</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>cft_rows_5</td>\n",
       "      <td>AAB14</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Türkisch</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Italienisch</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Social sciences, journalism and information</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 79 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        task_id subject   age  gender  handedness  native_german  \\\n",
       "0    cft_rows_1   AAB14  19.0     2.0         1.0            1.0   \n",
       "1    cft_rows_2   AAB14  19.0     2.0         1.0            1.0   \n",
       "2    cft_rows_3   AAB14  19.0     2.0         1.0            1.0   \n",
       "3  cft_rows_4_e   AAB14  19.0     2.0         1.0            1.0   \n",
       "4    cft_rows_5   AAB14  19.0     2.0         1.0            1.0   \n",
       "\n",
       "   native_german_mother native_language_mother  native_german_father  \\\n",
       "0                   0.0               Türkisch                   0.0   \n",
       "1                   0.0               Türkisch                   0.0   \n",
       "2                   0.0               Türkisch                   0.0   \n",
       "3                   0.0               Türkisch                   0.0   \n",
       "4                   0.0               Türkisch                   0.0   \n",
       "\n",
       "  native_language_father  ...  leisure_play_games  leisure_relaxation  \\\n",
       "0            Italienisch  ...                   0                   0   \n",
       "1            Italienisch  ...                   0                   0   \n",
       "2            Italienisch  ...                   0                   0   \n",
       "3            Italienisch  ...                   0                   0   \n",
       "4            Italienisch  ...                   0                   0   \n",
       "\n",
       "   leisure_social_activity  leisure_humanitarian_services  \\\n",
       "0                        1                              0   \n",
       "1                        1                              0   \n",
       "2                        1                              0   \n",
       "3                        1                              0   \n",
       "4                        1                              0   \n",
       "\n",
       "   leisure_nature_activities leisure_travel_tourism  \\\n",
       "0                          1                      0   \n",
       "1                          1                      0   \n",
       "2                          1                      0   \n",
       "3                          1                      0   \n",
       "4                          1                      0   \n",
       "\n",
       "                         study_subject_primary  study_subject_secondary  \\\n",
       "0  Social sciences, journalism and information                      NaN   \n",
       "1  Social sciences, journalism and information                      NaN   \n",
       "2  Social sciences, journalism and information                      NaN   \n",
       "3  Social sciences, journalism and information                      NaN   \n",
       "4  Social sciences, journalism and information                      NaN   \n",
       "\n",
       "   cft_task  cft_sum_full  \n",
       "0       1.0          28.0  \n",
       "1       1.0          28.0  \n",
       "2       1.0          28.0  \n",
       "3       1.0          28.0  \n",
       "4       1.0          28.0  \n",
       "\n",
       "[5 rows x 79 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tiq = pd.read_csv('./data/cft_full.csv')  # you may need to substitute this with a different path to the data\n",
    "tiq.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17010, 79)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tiq.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. General Analysis\n",
    "\n",
    "Next, we investigate frequencies and distributions of the raw (unprocessed) data set.\n",
    "\n",
    "## Solved Tasks\n",
    "\n",
    "Here, we illustrate the **number of participants, who managed to solve each task correctly**. In addition, we show the incremental mean of participants and the number of unsolved tasks. Note that the tasks are shown in the order of appearance. Each block of tasks is highlighted in a different color. We also indicate all tasks with an error during the data collection process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "[]"
      ],
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "color_palette = ['#fee090','#91cf60','#e0f3f8','#91bfdb']\n",
    "tasks = tiq.groupby('task_id', sort=False)['cft_task'].sum()  # sum of participants who solved the task\n",
    "mean = pd.Series(tasks.values).expanding().mean()  # incremental mean no. of participants with correct answer\n",
    "\n",
    "# Count number of NaNs (i.e. no. of participants that did not solve the task)\n",
    "unsolved = tiq[['task_id','cft_task']].copy()\n",
    "unsolved['cft_task'] = unsolved['cft_task'].isnull()\n",
    "unsolved = unsolved.groupby('task_id', sort=False)['cft_task'].sum()\n",
    "\n",
    "itr = 0  # task iterator\n",
    "clr = 0  # color indicator\n",
    "group_size = [15,15,14,10]  # no. tasks per group\n",
    "\n",
    "fig = plt.figure(figsize=(20,5))\n",
    "\n",
    "for tk in tasks.items():\n",
    "    if itr == group_size[clr]:\n",
    "        itr = 0  # reset iterator (next task group begins)\n",
    "        clr += 1  # increment color indicator\n",
    "    \n",
    "    # Mark erroneous measurements with pattern\n",
    "    if tk[0][-1] == 'e':\n",
    "        pattern = '//'\n",
    "    else:\n",
    "        pattern = ''\n",
    "    \n",
    "    plt.bar(tk[0], tk[1], color=color_palette[clr], hatch=pattern, label='_nolegend_')\n",
    "    \n",
    "    itr += 1  # increment task iterator\n",
    "\n",
    "# Line plot of incremental mean\n",
    "plt.plot(unsolved.keys(), mean, color='black', lw=2, marker='.', markersize=12, label='Incremental Mean')\n",
    "\n",
    "# Line plot of unsolved tasks\n",
    "plt.plot(unsolved.keys(), unsolved.values, color='black', lw=2, ls=':', marker='x', markersize=8, label='Participants without an answer')\n",
    "    \n",
    "plt.ylabel('Participants with Correct Answer', size=12)\n",
    "plt.xlabel('Task', size=12)\n",
    "plt.xticks(rotation=90)\n",
    "plt.xlim(-1, 54)\n",
    "plt.ylim(0, 335)\n",
    "plt.legend()\n",
    "\n",
    "# Save histogram\n",
    "plt.savefig('./figures/task_histogram.pdf', bbox_inches='tight', format='pdf')\n",
    "plt.savefig('./figures/task_histogram.png', bbox_inches='tight', format='png')\n",
    "\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bias\n",
    "\n",
    "We **investigate whether the data is biased against age or gender**. To this end, we plot the distribution/frequency of both variables, as well as the normalized fraction of correctly solved tasks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generic function to plot distribution histogram (bar chart)\n",
    "def plot_bar(x, y_1, y_2, x_ticklabels, label_x, label_y, path, stacked=False):\n",
    "    wd = .4\n",
    "    \n",
    "    if stacked:\n",
    "        plt.bar(x, y_1, color='#91bfdb')\n",
    "        plt.bar(x, y_2, color='#fc8d59', bottom=y_1)\n",
    "    else:\n",
    "        plt.bar(x - wd/2, y_1, width=wd, color='#91bfdb')\n",
    "        plt.bar(x + wd/2, y_2, width=wd, color='#fc8d59')\n",
    "        plt.ylim(0,1)\n",
    "        \n",
    "    plt.xlabel(label_x, size=12)\n",
    "    plt.xticks(x, x_ticklabels)\n",
    "    plt.ylabel(label_y, size=12)\n",
    "    plt.xlim(min(x)-.5, max(x)+.5)\n",
    "    plt.legend(['Male','Female'], loc='upper right')\n",
    "    \n",
    "    plt.savefig('./figures/{}.png'.format(path), bbox_inches='tight', format='png')\n",
    "    plt.savefig('./figures/{}.pdf'.format(path), bbox_inches='tight', format='pdf')\n",
    "    \n",
    "    plt.show()\n",
    "##############################################################################\n",
    "\n",
    "# Plot histogram of age and gender\n",
    "age_count_male = tiq[tiq['gender'] == 1]['age'].value_counts().sort_index()  # count male participants per age\n",
    "age_count_female = tiq[tiq['gender'] == 2]['age'].value_counts().sort_index()  # count female participants per age\n",
    "\n",
    "# Correctly solved tasks per age and gender\n",
    "tasks_age_male = tiq[tiq['gender'] == 1].groupby('age', sort=False)['cft_task'].sum().sort_index()\n",
    "tasks_age_female = tiq[tiq['gender'] == 2].groupby('age', sort=False)['cft_task'].sum().sort_index()\n",
    "\n",
    "\n",
    "plot_bar(age_count_male.keys(), age_count_male.values, age_count_female.values, \n",
    "         age_count_male.keys().values.astype(int), 'Age', 'No. of Tasks', 'age_hist', True)\n",
    "plot_bar(age_count_male.keys(), tasks_age_male.values / age_count_male.values, tasks_age_female.values / age_count_female.values,\n",
    "         age_count_male.keys().values.astype(int), 'Age', 'Norm. % of Tasks Solved Correctly', 'age_tasks_norm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CFT Distribution\n",
    "\n",
    "We investigate the histogram of the aggregated CFT score (cft_sum_full) and find that it is approximately normally distributed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cft_unique = tiq[['subject', 'cft_sum_full']].drop_duplicates()  # extract unique subjects\n",
    "\n",
    "cft_unique.hist(bins=26, color='#91bfdb')  # separate into 26 bins, since there are 26 unique aggr. CFT scores\n",
    "\n",
    "plt.grid(False)\n",
    "plt.title(None)\n",
    "plt.xlabel('Aggregated CFT', size=12)\n",
    "plt.ylabel('No. of Participants', size=12)\n",
    "\n",
    "# Save histogram\n",
    "plt.savefig('./figures/cft_histogram.png', bbox_inches='tight', format='png')\n",
    "plt.savefig('./figures/cft_histogram.pdf', bbox_inches='tight', format='pdf')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Preprocessing\n",
    "\n",
    "All subsequent analysis requires the data set to be preprocessed. For illustration, we consider a binary classification scenario of **cft_task (target)**. Specifically, we use the **true class \"correct answers (label 1)\"** and the **negative class \"wrong answers (label 0)\"**. We ignore missing answers (NaN values) in this experiment (removes 1,248 observations).\n",
    "\n",
    "Specifically, we apply the following pre-processing steps:\n",
    "1. Specify the categorical and the continuous features (according to the paper)\n",
    "1. Remove all observations with missing target (i.e. NaN value in cft_task).\n",
    "2. Remove subject and cft_sum_full to avoid information leakage due to high dependendy with the target.\n",
    "3. Impute NaN-values in categorical features with a new category.\n",
    "4. Impute NaN-values in continuous features with the median.\n",
    "5. Factorize the categorical features (to encode strings as integers).\n",
    "6. Normalize the continuous features to the intervall [0,1] (since we will be using l2-regularization).\n",
    "7. Split data into a training and test set (20% holdout for testing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Extract categorical and continuous features\n",
    "ftr_cont = ['mean_grade_degree','grades_math', 'grades_german', 'grades_biology', \n",
    "            'grades_physics', 'grades_chemistry','grades_geography','grades_history','grades_art',\n",
    "            'gaming_hours_weekly_min', 'gaming_hours_weekly_max', 'cft_sum_full']\n",
    "ftr_cat = tiq.iloc[:,~tiq.columns.isin(ftr_cont)].columns.tolist()\n",
    "\n",
    "# Remove the target 'cft_task' from the list of categorical features\n",
    "ftr_cat.remove('cft_task')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2. Remove all observations with missing target\n",
    "tiq = tiq[tiq['cft_task'].notna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3. Remove subject and cft_sum_full, due to possible information leakage   \n",
    "# Note that we did NOT remove these variables for the computation of the distance correlation as reported in the paper\n",
    "# However, it should be noted that none of these variables has a pairwise correlation above 0.5\n",
    "tiq = tiq.drop(['subject','cft_sum_full'], axis=1)\n",
    "ftr_cat.remove('subject')\n",
    "ftr_cont.remove('cft_sum_full')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 4. Impute NaN-values in categorical features with a new category\n",
    "tiq[ftr_cat] = tiq[ftr_cat].fillna('new_category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 5. Impute NaN-values in continuous features with the median\n",
    "medians = tiq[ftr_cont].median()\n",
    "tiq[ftr_cont] = tiq[ftr_cont].fillna(medians)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 6. Factorize the categorical features\n",
    "tiq[ftr_cat] = tiq[ftr_cat].apply(lambda x: pd.factorize(x)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 7. Normalize the continuous features\n",
    "tiq[ftr_cont] = MinMaxScaler().fit_transform(tiq[ftr_cont])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 8. Train/Test split of the data\n",
    "X_train, X_test, y_train, y_test = train_test_split(tiq.drop('cft_task', axis=1), tiq['cft_task'], test_size=.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Distance Correlations\n",
    "\n",
    "We compute the pairwise Distance Correlation of features in a test set according to\n",
    "\n",
    "Székely, Gábor J., Maria L. Rizzo, and Nail K. Bakirov. \"Measuring and testing dependence by correlation of distances.\" The annals of statistics 35.6 (2007): 2769-2794."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Falling back to uncompiled AVL fast distance covariance because of TypeError exception raised: No matching definition for argument type(s) array(int64, 1d, C), array(int64, 1d, C), bool. Rembember: only floating point values can be used in the compiled implementations.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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     "text": [
      "Falling back to uncompiled AVL fast distance covariance because of TypeError exception raised: No matching definition for argument type(s) array(int64, 1d, C), array(float64, 1d, C), bool. Rembember: only floating point values can be used in the compiled implementations.\n"
     ]
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      "['year_of_degree', 'leisure_play_games', 0.06281303372251992]\n",
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      "['year_of_degree', 'cft_sum_full', 0.0798333950739058]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Falling back to uncompiled AVL fast distance covariance because of TypeError exception raised: No matching definition for argument type(s) array(float64, 1d, C), array(int64, 1d, C), bool. Rembember: only floating point values can be used in the compiled implementations.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "['leisure_play_games', 'cft_sum_full', 0.058282865349557504]\n",
      "['leisure_relaxation', 'leisure_social_activity', 0.17683234780865234]\n",
      "['leisure_relaxation', 'leisure_humanitarian_services', 0.01420112741473795]\n",
      "['leisure_relaxation', 'leisure_nature_activities', 0.039681832730289364]\n",
      "['leisure_relaxation', 'leisure_travel_tourism', 0.030382365945524167]\n",
      "['leisure_relaxation', 'study_subject_primary', 0.05153149403636726]\n",
      "['leisure_relaxation', 'study_subject_secondary', 0.04843809866671648]\n",
      "['leisure_relaxation', 'cft_task', 0.019641939562577548]\n",
      "['leisure_relaxation', 'cft_sum_full', 0.06155338696426651]\n",
      "['leisure_social_activity', 'leisure_humanitarian_services', 0.03154965803817859]\n",
      "['leisure_social_activity', 'leisure_nature_activities', 0.014202702995064577]\n",
      "['leisure_social_activity', 'leisure_travel_tourism', 0.006418534199003056]\n",
      "['leisure_social_activity', 'study_subject_primary', 0.06780483739300225]\n",
      "['leisure_social_activity', 'study_subject_secondary', 0.08085976686840178]\n",
      "['leisure_social_activity', 'cft_task', 0.004621636525954268]\n",
      "['leisure_social_activity', 'cft_sum_full', 0.06399610044200672]\n",
      "['leisure_humanitarian_services', 'leisure_nature_activities', 0.0017857568333701204]\n",
      "['leisure_humanitarian_services', 'leisure_travel_tourism', 0.007224518966874217]\n",
      "['leisure_humanitarian_services', 'study_subject_primary', 0.027897168039845332]\n",
      "['leisure_humanitarian_services', 'study_subject_secondary', 0.07407708733865033]\n",
      "['leisure_humanitarian_services', 'cft_task', 0.006484018151457149]\n",
      "['leisure_humanitarian_services', 'cft_sum_full', 0.05694184383861588]\n",
      "['leisure_nature_activities', 'leisure_travel_tourism', 0.11838792214980684]\n",
      "['leisure_nature_activities', 'study_subject_primary', 0.04582282786542804]\n",
      "['leisure_nature_activities', 'study_subject_secondary', 0.07505132670850664]\n",
      "['leisure_nature_activities', 'cft_task', 0.023472663750277355]\n",
      "['leisure_nature_activities', 'cft_sum_full', 0.03269587095851207]\n",
      "['leisure_travel_tourism', 'study_subject_primary', 0.06366797598807294]\n",
      "['leisure_travel_tourism', 'study_subject_secondary', 0.09127705377774158]\n",
      "['leisure_travel_tourism', 'cft_task', 0.0083370287922968]\n",
      "['leisure_travel_tourism', 'cft_sum_full', 0.1270980743788916]\n",
      "['study_subject_primary', 'study_subject_secondary', 0.2795805997860173]\n",
      "['study_subject_primary', 'cft_task', 0.01747816310921396]\n",
      "['study_subject_primary', 'cft_sum_full', 0.09255408873519665]\n",
      "['study_subject_secondary', 'cft_task', 0.021648739144771518]\n",
      "['study_subject_secondary', 'cft_sum_full', 0.10769449315402975]\n",
      "['cft_task', 'cft_sum_full', 0.20897906860433443]\n"
     ]
    }
   ],
   "source": [
    "# Compute Distance Correlation (on a sample of the data)\n",
    "# Note that we also provide pre-computed distance correlation scores, which can be loaded in the next cell!\n",
    "dist_crl = []\n",
    "combs = set()  # save feature combinations\n",
    "crl_sample = tiq.sample(frac=0.2, random_state=42)  # sample a test set (as computation of distance correlation is costly) \n",
    "\n",
    "for ftr1 in crl_sample:\n",
    "    for ftr2 in crl_sample:\n",
    "        # Check if feature combination was already evaluated (distance correlation is symmetric!)\n",
    "        if '{}-{}'.format(ftr1, ftr2) not in combs and '{}-{}'.format(ftr2, ftr1) not in combs and ftr1 != ftr2:\n",
    "            combs.add('{}-{}'.format(ftr1, ftr2))  # add feature pair to list of combinations\n",
    "            dist_crl.append([ftr1, ftr2, dcor.distance_correlation(crl_sample[ftr1], crl_sample[ftr2])])\n",
    "            print(dist_crl[-1])\n",
    "\n",
    "dist_crl = pd.DataFrame(dist_crl, columns=['Feature 1', 'Feature 2', 'Dist. Correlation'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save/Load the Distance correlation object\n",
    "#filehandler = open('./objects/distance_correlation_full.obj', 'wb')\n",
    "#pickle.dump(dist_crl, filehandler)  # Save Object\n",
    "filehandler = open('./objects/distance_correlation.obj', 'rb')\n",
    "dist_crl = pickle.load(filehandler)  # Load Object\n",
    "filehandler.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{llr}\n",
      "\\toprule\n",
      "                   Feature 1 &                    Feature 2 &  Dist. Correlation \\\\\n",
      "\\midrule\n",
      "                  gaming\\_mmo &                gaming\\_racing &            1.00000 \\\\\n",
      "                     smoking &           excessive\\_drinking &            1.00000 \\\\\n",
      "     gaming\\_hours\\_weekly\\_min &      gaming\\_hours\\_weekly\\_max &            0.99517 \\\\\n",
      "            gaming\\_adventure &                gaming\\_action &            0.93618 \\\\\n",
      "               gaming\\_action &                gaming\\_casual &            0.93020 \\\\\n",
      "               gaming\\_casual &                   gaming\\_rpg &            0.91362 \\\\\n",
      "               gaming\\_action &                gaming\\_sports &            0.90698 \\\\\n",
      "            gaming\\_adventure &                gaming\\_sports &            0.90695 \\\\\n",
      "               gaming\\_casual &                gaming\\_sports &            0.90007 \\\\\n",
      "               gaming\\_action &                   gaming\\_rpg &            0.89854 \\\\\n",
      "            gaming\\_adventure &                gaming\\_casual &            0.89358 \\\\\n",
      "               gaming\\_casual &              gaming\\_strategy &            0.88825 \\\\\n",
      "            gaming\\_adventure &                   gaming\\_rpg &            0.87973 \\\\\n",
      "                  gaming\\_rpg &                gaming\\_sports &            0.87833 \\\\\n",
      "               gaming\\_action &              gaming\\_strategy &            0.87555 \\\\\n",
      "                  gaming\\_rpg &              gaming\\_strategy &            0.86418 \\\\\n",
      "               gaming\\_sports &              gaming\\_strategy &            0.86186 \\\\\n",
      "        native\\_german\\_father &       native\\_language\\_father &            0.85660 \\\\\n",
      "               gaming\\_casual &                gaming\\_racing &            0.85643 \\\\\n",
      "               gaming\\_casual &                   gaming\\_mmo &            0.85643 \\\\\n",
      "            gaming\\_adventure &              gaming\\_strategy &            0.85103 \\\\\n",
      "               gaming\\_casual &            gaming\\_simulation &            0.84677 \\\\\n",
      "               gaming\\_action &                   gaming\\_mmo &            0.82699 \\\\\n",
      "               gaming\\_action &                gaming\\_racing &            0.82699 \\\\\n",
      "               gaming\\_racing &                   gaming\\_rpg &            0.82624 \\\\\n",
      "                  gaming\\_mmo &                   gaming\\_rpg &            0.82624 \\\\\n",
      "            gaming\\_adventure &                gaming\\_racing &            0.82463 \\\\\n",
      "            gaming\\_adventure &                   gaming\\_mmo &            0.82463 \\\\\n",
      "               gaming\\_racing &                gaming\\_sports &            0.82424 \\\\\n",
      "                  gaming\\_mmo &                gaming\\_sports &            0.82424 \\\\\n",
      "               gaming\\_action &            gaming\\_simulation &            0.82163 \\\\\n",
      "                  gaming\\_rpg &            gaming\\_simulation &            0.81015 \\\\\n",
      "            gaming\\_adventure &            gaming\\_simulation &            0.80030 \\\\\n",
      "           gaming\\_simulation &                gaming\\_sports &            0.79692 \\\\\n",
      "        native\\_german\\_mother &       native\\_language\\_mother &            0.79633 \\\\\n",
      "           gaming\\_simulation &              gaming\\_strategy &            0.79156 \\\\\n",
      "               gaming\\_racing &              gaming\\_strategy &            0.78592 \\\\\n",
      "                  gaming\\_mmo &              gaming\\_strategy &            0.78592 \\\\\n",
      "               gaming\\_racing &            gaming\\_simulation &            0.78168 \\\\\n",
      "                  gaming\\_mmo &            gaming\\_simulation &            0.78168 \\\\\n",
      "        native\\_german\\_mother &         native\\_german\\_father &            0.72413 \\\\\n",
      " gaming\\_first\\_person\\_shooter &                   gaming\\_rpg &            0.72325 \\\\\n",
      "           mean\\_grade\\_degree &                  grades\\_math &            0.72119 \\\\\n",
      "            gaming\\_adventure &  gaming\\_first\\_person\\_shooter &            0.70774 \\\\\n",
      "               gaming\\_action &  gaming\\_first\\_person\\_shooter &            0.70380 \\\\\n",
      " gaming\\_first\\_person\\_shooter &                gaming\\_casual &            0.70012 \\\\\n",
      "        native\\_german\\_mother &       native\\_language\\_father &            0.67905 \\\\\n",
      " gaming\\_first\\_person\\_shooter &                gaming\\_sports &            0.67386 \\\\\n",
      "      native\\_language\\_mother &       native\\_language\\_father &            0.66508 \\\\\n",
      " gaming\\_first\\_person\\_shooter &              gaming\\_strategy &            0.65446 \\\\\n",
      " gaming\\_first\\_person\\_shooter &            gaming\\_simulation &            0.64308 \\\\\n",
      " gaming\\_first\\_person\\_shooter &                gaming\\_racing &            0.63502 \\\\\n",
      " gaming\\_first\\_person\\_shooter &                   gaming\\_mmo &            0.63502 \\\\\n",
      "      native\\_language\\_mother &         native\\_german\\_father &            0.63094 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Plot most strongly correlated features\n",
    "top_crl = dist_crl.reindex(dist_crl['Dist. Correlation'].sort_values(ascending=False).index).reset_index(drop=True)  # sort values\n",
    "top_crl['Dist. Correlation'] = top_crl['Dist. Correlation'].round(5)  # round scores\n",
    "top_crl = top_crl.iloc[:54,:]  # select top entries\n",
    "\n",
    "print(top_crl.to_latex(index=False))  # plot in latex style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot histogram of the distance correlation scores\n",
    "def plot_hist(var, b, xmin, xlabel, ylabel, path):\n",
    "    var.hist(bins=b, color='#91bfdb')\n",
    "\n",
    "    plt.grid(False)\n",
    "    plt.xlim(xmin, 1)\n",
    "    plt.title(None)\n",
    "    plt.xlabel(xlabel, size=12)\n",
    "    plt.ylabel(ylabel, size=12)\n",
    "\n",
    "    # Save histogram\n",
    "    plt.savefig('./figures/{}_histogram.png'.format(path), bbox_inches='tight', format='png')\n",
    "    plt.savefig('./figures/{}_histogram.pdf'.format(path), bbox_inches='tight', format='pdf')\n",
    "\n",
    "    plt.show()\n",
    "##############################################################################\n",
    "\n",
    "plot_hist(dist_crl, 100, 0, 'Distance Correlation', 'No. of Feature Pairs', 'dist_corr')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Logistic Regression Model\n",
    "\n",
    "For illustration, we train a simple Logistic Regression model to predict the **cft_task (target)**. We also compute the **SHAP and LIME values** to quantify every feature's contribution in the prediction.\n",
    "\n",
    "Specifically, we perform the following training and evaluation steps:\n",
    "1. Train and test the Logistic Regression (LR) model\n",
    "2. Plot the weights of the LR model\n",
    "3. Compute Shapley values for the LR model\n",
    "4. Compute LIME values for the LR model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to plot ROC curve\n",
    "def plot_roc(y_pred, path):\n",
    "    # Compute ROC-AUC Score\n",
    "    auc = roc_auc_score(y_test, y_pred)\n",
    "\n",
    "    # Plot ROC Curve\n",
    "    fpr, tpr, _ = roc_curve(y_test, y_pred)\n",
    "\n",
    "    plt.plot(fpr, tpr, color='#4575b4', label='ROC Curve (AUC = {})'.format(round(auc, 4)), lw=2)\n",
    "    plt.plot([0, 1], [0, 1], color='black', ls='--', label='_nolegend_', lw=2)\n",
    "    plt.xlim(0,1)\n",
    "    plt.ylim(0,1)\n",
    "    plt.xlabel('False Positive Rate', size=12)\n",
    "    plt.ylabel('True Positive Rate', size=12)\n",
    "    plt.legend()\n",
    "\n",
    "    plt.savefig('{}.pdf'.format(path), bbox_inches='tight', format='pdf')  # save as PDF\n",
    "    plt.savefig('{}.png'.format(path), bbox_inches='tight', format='png')  # save as PNG    \n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1. Train and test the LR model\n",
    "model = LogisticRegression(penalty='l2', random_state=42, max_iter=1000, solver='lbfgs')\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "y_pred = model.predict_proba(X_test)\n",
    "plot_roc(y_pred[:,1], './figures/roc_lr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2. Plot the weights of the LR model\n",
    "coef = model.coef_.flatten()\n",
    "coef_idx = abs(coef).argsort()[-20:] # get indices of top coefficients\n",
    "coef_sorted = coef[coef_idx]\n",
    "coef_names = X_test.columns[coef_idx]  # get coefficient names\n",
    "\n",
    "fig = plt.figure(figsize=(8,6))\n",
    "plt.xlabel('Logistic Regression Coefficient', size=12)\n",
    "plt.ylabel('Feature', size=12)\n",
    "plt.barh(coef_names, coef_sorted, color='#4575b4')\n",
    "plt.ylim(-1,20)\n",
    "plt.vlines(0, -1, 21, ls='--')\n",
    "\n",
    "plt.savefig('./figures/coef_lr.pdf', bbox_inches='tight', format='pdf')  # save as PDF\n",
    "plt.savefig('./figures/coef_lr.png', bbox_inches='tight', format='png')  # save as PNG  \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x684 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 3. Compute SHAP (LinearSHAP)\n",
    "reference_X = shap.sample(X_train, 100)\n",
    "explainer = shap.LinearExplainer(model, reference_X)\n",
    "shap_values = explainer.shap_values(X_test)\n",
    "shap.summary_plot(shap_values, X_test, show=False)\n",
    "\n",
    "plt.savefig('./figures/shap_lr.png', bbox_inches='tight', format='png')\n",
    "plt.savefig('./figures/shap_lr.pdf', bbox_inches='tight', format='pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 4. Compute LIME (TabularLIME)\n",
    "lime_exp = lime_tabular.LimeTabularExplainer(\n",
    "    reference_X.to_numpy(), \n",
    "    feature_names=X_test.columns.values.tolist(),\n",
    "    discretize_continuous=False,\n",
    "    random_state=42\n",
    ")\n",
    "\n",
    "lime_mean = np.zeros(X_test.shape[1])\n",
    "\n",
    "for xt in X_test.to_numpy(): # Compute average LIME scores\n",
    "    exp = lime_exp.explain_instance(xt, model.predict_proba, num_samples=100, num_features=X_test.shape[1], top_labels=2).local_exp\n",
    "    \n",
    "    for itm in exp[1]:  # collect explanations for positive class (label=1)\n",
    "        lime_mean[itm[0]] += itm[1]  # sum local explanations\n",
    "        \n",
    "lime_mean /= X_test.shape[0]  # compute mean attribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lime_mean_idx = abs(lime_mean).argsort()[-20:] # get indices of top coefficients\n",
    "lime_mean_sorted = lime_mean[lime_mean_idx]\n",
    "coef_names = X_test.columns[lime_mean_idx]  # get coefficient names\n",
    "\n",
    "fig = plt.figure(figsize=(8,6))\n",
    "plt.xlabel('Average LIME Attribution', size=12)\n",
    "plt.ylabel('Feature', size=12)\n",
    "plt.barh(coef_names, lime_mean_sorted, color='#4575b4')\n",
    "plt.ylim(-1,20)\n",
    "plt.vlines(0, -1, 21, ls='--')\n",
    "\n",
    "plt.savefig('./figures/lime_lr.pdf', bbox_inches='tight', format='pdf')  # save as PDF\n",
    "plt.savefig('./figures/lime_lr.png', bbox_inches='tight', format='png')  # save as PNG  \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
